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Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-linear Chaining Extended

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Book cover Research in Computational Molecular Biology (RECOMB 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10812))

Abstract

Aligning sequencing reads on graph representations of genomes is an important ingredient of pan-genomics. Such approaches typically find a set of local anchors that indicate plausible matches between substrings of a read to subpaths of the graph. These anchor matches are then combined to form a (semi-local) alignment of the complete read on a subpath. Co-linear chaining is an algorithmically rigorous approach to combine the anchors. It is a well-known approach for the case of two sequences as inputs. Here we extend the approach so that one of the inputs can be a directed acyclic graph (DAGs), e.g. a splicing graph in transcriptomics or a variant graph in pan-genomics.

This extension to DAGs turns out to have a tight connection to the minimum path cover problem, asking us to find a minimum-cardinality set of paths that cover all the nodes of a DAG. We study the case when the size k of a minimum path cover is small, which is often the case in practice. First, we propose an algorithm for finding a minimum path cover of a DAG (V, E) in \(O(k|E|\log |V|)\) time, improving all known time-bounds when k is small and the DAG is not too dense. Second, we introduce a general technique for extending dynamic programming (DP) algorithms from sequences to DAGs. This is enabled by our minimum path cover algorithm, and works by mimicking the DP algorithm for sequences on each path of the minimum path cover. This technique generally produces algorithms that are slower than their counterparts on sequences only by a factor k. Our technique can be applied, for example, to the classical longest increasing subsequence and longest common subsequence problems, extended to labeled DAGs. Finally, we apply this technique to the co-linear chaining problem, which is a generalization of both of these two problems. We also implemented the new co-linear chaining approach. Experiments on splicing graphs show that the new method is efficient also in practice.

A. Tomescu and V. Mäkinen—Shared last author contribution.

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Acknowledgements

We thank the anonymous reviewers for comments that improved the presentation of this paper. We thank Gonzalo Navarro for pointing out the connection to pattern matching on hypertexts. This work was funded in part by the Academy of Finland (grant 274977 to AIT and grants 284598 and 309048 to AK and to VM), and by Futurice Oy (to TP).

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Authors and Affiliations

  1. Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland

    Anna Kuosmanen, Topi Paavilainen, Alexandru Tomescu & Veli Mäkinen

  2. Diego Portales University, Santiago, Chile

    Travis Gagie

  3. CNRS, CRIStAL, University of Lille 1, Villeneuve-d’Ascq, France

    Rayan Chikhi

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  1. Anna Kuosmanen
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Correspondence to Veli Mäkinen .

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  1. Computer Science Department, Princeton University, Princeton, New Jersey, USA

    Benjamin J. Raphael

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Kuosmanen, A., Paavilainen, T., Gagie, T., Chikhi, R., Tomescu, A., Mäkinen, V. (2018). Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-linear Chaining Extended. In: Raphael, B. (eds) Research in Computational Molecular Biology. RECOMB 2018. Lecture Notes in Computer Science(), vol 10812. Springer, Cham. https://doi.org/10.1007/978-3-319-89929-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-89929-9_7

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